Context Data
  • Welcome to the Context Data Documentation
  • Quickstart
  • Adding Source Connections
  • Adding Embedding Models
  • Adding Target Connections
  • Create Transformations
  • Create and Run Flows
  • API reference
    • Auth
    • Workspace
      • Source connections
      • Embedding models
      • Chat models
      • Target connections
      • Flows
      • Vector Store Query
      • Query sessions
  • API Documentation (Redoc)
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Quickstart

PreviousWelcome to the Context Data DocumentationNextAdding Source Connections

Last updated 10 months ago

There are four basic steps to build an end-to-end flow on Context Data

1). Source Connection: Build connection(s) to where your source data resides (e.g. MySQL, PostgreSQL, Amazon S3)

2). Embedding Model: Create a link to the embedding model which will convert data retrieved from the source to vector embeddings (basically an array of numbers)

3). Target Connection: Build connection(s) to where the vector embeddings will be saved (and where your AI application will read from)

4). Flow: The flow ties of the steps above (source connection, embedding model and target connection) into an end-to-end process ready to be executed.

Basically, when a flow is triggered, it will:

  • Get the data from the source connection that you defined

  • Convert the retrieved data to a format optimized for vector search

  • Write the converted data to the vector database/store